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act_layer

Syntologyentry name in harvested coderead from the graph 2026-09-24

act_layer appears in the code Syntology harvested for 20 papers, as 11 distinct code bodies found in 26 places (a place is one code body under one paper). At least one of them ran in 12 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named act_layer do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 5 of the 11 distinct code bodies named act_layer; 6 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
3ran · our draft was wrong
0ran · fixture could not drive it
2ran
6unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 26 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

20 papers shown of 20, newest first; 26 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Towards Model-Agnostic Dataset Condensation by Heterogeneous Models 22 Sep 2024 KHU-AGI/HMDC/gcn_lib/torch_nn.py 9cb7add8f210b472 ran · our draft was wrong no licence file found · pointer only
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks 29 Jun 2024 markusgrotz/peract_bimanual/helpers/network_utils.py c99fb7b9654ea364 ran Apache-2.0 (permissive)
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation 11 May 2024 SLDGroup/EMCAD/lib/decoders.py bb51501828a7b35a ran · our draft was wrong licence not identified · pointer only
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training 22 Feb 2024 peract/peract/helpers/network_utils.py c99fb7b9654ea364 ran Apache-2.0 (permissive)
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations 2024-02 (from id) eyringmlclimategroup/behrens24james_SPCESM2_ML_ensembles/cbrain/models.py 11520a2864c629c0 unverified MIT (permissive)
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations 2024-02 (from id) eyringmlclimategroup/behrens24james_SPCESM2_ML_ensembles/cbrain/legacy/models.py 413e979484c437c6 unverified MIT (permissive)
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields 31 Aug 2023 YanjieZe/GNFactor/GNFactor/helpers/network_utils.py c99fb7b9654ea364 ran MIT (permissive)
Unsupervised Multiplex Graph Learning with Complementary and Consistent Information 3 Aug 2023 larryuestc/cocomg/models/Layers.py 97c6d61cd9f6bb59 ran no licence file found · pointer only
A Universal Semantic-Geometric Representation for Robotic Manipulation 18 Jun 2023 TongZhangTHU/sgr/helpers/network_utils.py c99fb7b9654ea364 ran Apache-2.0 (permissive)
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models 14 Apr 2023 zyh16143998882/IDPT/models/Point_MAE.py ed4cd8f3b2b83784 ran · our draft was wrong no licence file found · pointer only
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes 9 Apr 2023 arnold-benchmark/arnold/bc_z/blocks.py c99fb7b9654ea364 ran MIT (permissive)
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation 9 Apr 2023 xiangchenyin/grpose/gcn_lib/torch_nn.py 9cb7add8f210b472 ran · our draft was wrong Apache-2.0 (permissive)
Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models 23 Jul 2022 columbia-ai-robotics/semantic-abstraction/arm/network_utils.py c99fb7b9654ea364 ran MIT (permissive)
Vision GNN: An Image is Worth Graph of Nodes 1 Jun 2022 gswycf/signgraph/modules/gcn_lib/torch_vertex.py 9cb7add8f210b472 ran · our draft was wrong no licence file found · pointer only
Climate-Invariant Machine Learning 14 Dec 2021 tbeucler/CBRAIN-CAM/cbrain/models.py 17416d46279a0a1c unverified MIT (permissive)
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction 29 Oct 2021 elichienxD/deep_gcns_torch/gcn_lib/dense/torch_nn.py 81d31aad45664fa2 unverified MIT (permissive)
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction 29 Oct 2021 elichienxD/deep_gcns_torch/gcn_lib/sparse/torch_nn.py 6b65956f32ad8f13 unverified MIT (permissive)
Training Graph Neural Networks with 1000 Layers 14 Jun 2021 lightaime/deep_gcns_torch/gcn_lib/dense/torch_nn.py 81d31aad45664fa2 unverified MIT (permissive)
Training Graph Neural Networks with 1000 Layers 14 Jun 2021 lightaime/deep_gcns_torch/gcn_lib/sparse/torch_nn.py 6b65956f32ad8f13 unverified MIT (permissive)
Robust Optimization as Data Augmentation for Large-scale Graphs 19 Oct 2020 devnkong/FLAG/deep_gcns_torch/gcn_lib/dense/torch_nn.py 81d31aad45664fa2 unverified MIT (permissive)
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems 2019-09 (from id) gunnarbehrens/cbrain-cam/cbrain/models.py 11520a2864c629c0 unverified MIT (permissive)
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems 2019-09 (from id) raspstephan/CBRAIN-CAM/cbrain/legacy/models.py 413e979484c437c6 unverified MIT (permissive)
Deep learning to represent sub-grid processes in climate models 12 Jun 2018 gmooers96/CBRAIN-CAM/cbrain/models.py 11520a2864c629c0 unverified MIT (permissive)
Deep learning to represent sub-grid processes in climate models 12 Jun 2018 gmooers96/CBRAIN-CAM/cbrain/legacy/models.py 413e979484c437c6 unverified MIT (permissive)
Deep learning to represent sub-grid processes in climate models 12 Jun 2018 jordanott/CBRAIN-CAM/cbrain/models.py 17416d46279a0a1c unverified MIT (permissive)
arXiv:aaai_28607 GuangmingZhu/SketchESC/models/graph_model.py d0e9cac8641039a9 unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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